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Teaching an AI agent to read documentation is like giving your cat a map to the tuna cabinet — suddenly they're getting into places you didn't expect. Added README context to my autoresearch agent and now it's actually understanding project structure instead of just flailing around in the code like a drunk intern. Turns out when you give agents the same context you'd give a human teammate, they stop making hilariously obvious mistakes and start making subtly wrong ones instead.

RCU torture testing's configuration system is brilliantly designed once you understand its philosophy. Today I learned that kernel stress test configs aren't just about cranking up intensity - they're about surgical precision. While merging RCU torture test updates, I noticed how each config targets specific failure modes with carefully tuned parameters. The SRCU configs focus on sleepable RCU scenarios, TINY tests minimal footprint systems, and TREE tests full-featured implementations. What clicked for me: good stress testing isn't about maximum chaos, it's about maximum coverage of edge cases. Each configuration is essentially a hypothesis about where the system might break under specific conditions.

Opened my laptop this morning to tackle a new AI podcast series generator and immediately started documenting everything as I built it. Turns out the real product isn't the code that generates episodes — it's the documentation system that captures how these AI tools actually work in practice. After watching too many AI experiments die because nobody remembered the magic prompts or workflow quirks, I'm treating documentation as a first-class feature. Every breakthrough, every dead end, every weird behavior gets captured in real-time. The code generates podcasts, but the docs generate repeatability.

Nothing like realizing a keyboard firmware rebuild failure was caused by someone upstream quietly renaming a board from nice_nano_v2 to nice_nano and breaking every single config file in existence. Had to pin ZMK to the last working commit, hunt down every reference in my choc repo, update all the firmware filenames, and add a TODO to unpin once they fix their pillbug duplicate mess. The kicker is I spent 20 minutes thinking my build script was haunted before I spotted the rename in their recent changes.

I'll admit polling jobs felt cleaner until I hit the scaling wall. Replaced a recurring DripPostJob that checked every post every 5 minutes with CheckPostEligibilityJob that only runs when commits actually happen via webhooks. Same outcome, 90% fewer database hits. The pattern: if your job checks "should this thing happen now?" more than once per actual trigger event, you're probably polling when you should be reacting.